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With multiple channels, Polarimetric SAR (PolSAR) contains abundant target information and anti-jamming ability, which can improve the ability of target discrimination and image interpretation. The classification problem of PolSAR has become one of the most urgent problems to be solved in PolSAR application with the improvement of PolSAR technology. Due to the complexity of multiple-dimensional classification,...
The paper presents the comparison of two genetic methods that can be used for feature selection, NSGA (Nondominated Sorting Genetic Algorithm) and GAAM (genetic algorithm with aggressive mutation). While the first method is very popular for optimizing multi-objective functions, the second one is a new method that was introduced just two years ago. The comparison was made with a benchmark file from...
Data mining has been an active area of research for the past couple of decades. Classification is an important data mining technique that consists of assigning a data instance to one of the several predefined categories. Various successful methods have already been suggested and tested to solve the problems of the classification. In this paper, author proposed a new hybrid classifier by combining...
Multi-population genetic algorithms have been used with success for several multi-objective optimization problems. In this paper, we present a new general multi-population genetic algorithm for evolving decision trees. It was designed to improve the possibility of evolving balanced decision trees, simultaneously optimized for the predictions of each class. Single-population genetic algorithms namely...
Learning rate and momentum coefficient are critical parameters on back propagation algorithm because of their effect on learning speed and deviation ratio from global minimum. Hidden neuron number has an effect on classification accuracy, and excessive number of hidden neuron causes to increase the operation load. Because these parameters are selected randomly, finding the accurate values requires...
Aiming at the problem of low accuracy in intrusion detection system, this paper established a genetic support vector machine (SVM) model according to the features of genetic algorithm and support vector machine algorithm. The model firstly optimizes the support vector parameters according to genetic algorithm, then we build the intrusion detection model with support vector machine optimized and use...
The wide application of Internet technology and media technology produces more and more data which also leads the arrival of the era of big data. However, it is difficult to extract the needed information from the original data directly except some special conditions. In recent years, the development of machine learning which provide a effective way to solve this problem for us. You can obtain lower...
Feature selection is one of the important preprocessing steps in analyzing high dimensional datasets. In this paper, first the ensemble of three different filter ranking methods including: Information Gain (IG), ReliefF and F-score are used to reduce the dimension of datasets. Afterward, reduced data are utilized as inputs of the meta-heuristic algorithm, Improved Binary Gravitational Search Algorithm...
One of the most interesting and important issues in the machine learning and data mining research areas is high accuracy classification. Imbalance data is a great challenge. The imbalance data is a kind of situation when the number of one data member class is significantly smaller than the other class. In the recent years this issue has got more attention among many researchers all over the world...
In the automotive industry the issue of safety remains a major priority. This aspect is not focused just on the driver but also on the other participants of the traffic like the pedestrians. This paper describes a pedestrian detection system where three different classification methods are used for detecting pedestrians with a far infrared camera. The three methods are tested and compared on variable...
Automatic image annotation (AIA) for a huge number of images is one of the most difficult challenging topics for researchers in the last two decades. For labeling images accurately, more various features containing low-level image features, textual tags of images have been extracted so far; however, not whole features give useful information for each conception. Feature selection as one of the important...
Due to the high dimensionality of hyperspectral data, dimension reduction is becoming an important problem in hyperspectral image classification. Band selection can retain the information which is capable of keeping the original meaning of the data, and thus has attracted more attention. This paper tackles the band selection problem from the perspective of multiple classifiers combination, which can...
Credit scoring is becoming a competitive issue with rapid growth and significant advance. Building a satisfactory credit model has attracted lots of researchers in the past decades and it is still one of the hottest research topics in the field of credit industry. This paper emphasizes on surveying the development of the artificial intelligence technologies for solving credit scoring. It covers algorithms...
The classification performance of Support Vector Machine (SVM) is heavily influenced by its kernel parameter g and penalty factor c. in this paper, Cross-validation (CV) based grid-search optimization, CV-based genetic algorithm (GA) and CV-based particle swarm optimization (PSO) are respectively used for parameters optimization in SVM for fault classification of inverters in traction converter. Simulation...
The use of Learning Management Systems (LMS) and the web-based formative and summative assessments during the traditional teaching in classroom provides the huge amount of data on students' behavior and results at the point of time when the course is still in progress. This data could be used for the final exam performance prediction so that the excellent as well as the students requiring help could...
The parameters of Support Vector Machine (SVM) are optimized using heuristic genetic algorithm and then to detect the network intrusion behavior. The heuristic real-coded genetic algorithm is used to optimize the best parameters of SVM with Gauss kernel aimed at the classification accuracy of the model. The classification accuracy is largely improved. Experimental results show that this method has...
Cluster is bunch of similar items. Unsupervised classification of patterns into clusters is known as clustering. It is useful in knowledge discovery in data. Clustering is able to deal with different data types. Fuzzy rules are used for data intelligence illustration purpose. User gets highly interpretable discovered clusters using fuzzy rules. To generate accurate fuzzy rules triangular membership...
Prototype selection aims at reducing the scale of datasets to improve prediction accuracy and operation efficiency by removing noisy or redundant patterns via the nearest neighbor classification algorithms. Genetic algorithms have been used recently for prototype selection and showed good performance, however, they have some drawbacks such as the deteriorated running effect, slow convergence for the...
We describe how a task in computer vision can be effectively resolved by employing Genetic Algorithm. This paper focuses on the problem of semantic segmentation of digital images. We propose to use an improved genetic algorithm for the learning parameters of weak classifiers in a boosting learning set up. We propose a new encoding and genetic operators in accordance with this problem. Beside that,...
In this paper, we present a weight learning method introduced to learn weights on each individual classifier to construct an ensemble. Genetic algorithm is applied to search for an optimal combination of weights for each individual classifier on which classifier ensemble is expected to give best performance. Our proposed ensemble approach can combine heterogeneous classifiers and/or classifier ensembles...
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